bart large cnn samsum

Providerphilschmid
Categorysummarization
Licensemit
Downloads106.2K
Stars0

Overview

The bart-large-cnn-samsum model is a specialized encoder-decoder transformer fine-tuned specifically for conversational summarization. Unlike general-purpose summarizers that struggle with the fragmented nature of chat logs, this model is optimized to distill dialogue into concise, coherent narratives while preserving speaker intent and key action items. For developers, this makes it an ideal choice for automating meeting minutes, condensing customer support tickets, or building AI-driven chat archives. It integrates seamlessly via the Hugging Face Transformers library, offering a predictable performance profile for NLP pipelines that require extractive-abstractive hybrid summaries without the latency of massive LLMs.

Highlights

  • Optimized for condensing conversational dialogue into narratives
  • Seamless integration via Hugging Face Transformers library
  • Efficient alternative to large-scale generative LLMs
  • Preserves speaker intent and critical action items
  • MIT licensed for flexible commercial deployment

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("philschmid/bart-large-cnn-samsum")
tokenizer = AutoTokenizer.from_pretrained("philschmid/bart-large-cnn-samsum")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download philschmid/bart-large-cnn-samsum

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download philschmid/bart-large-cnn-samsum config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('philschmid/bart-large-cnn-samsum')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/philschmid/bart-large-cnn-samsum

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/philschmid/bart-large-cnn-samsum

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('philschmid/bart-large-cnn-samsum')
tokenizer = AutoTokenizer.from_pretrained('philschmid/bart-large-cnn-samsum')

Full Documentation

来源: HuggingFace

---
language: en
license: mit
tags:

  • sagemaker

  • bart

  • summarization

datasets:
  • samsum

widget:
  • text: "Jeff: Can I train a \U0001F917 Transformers model on Amazon SageMaker? \n\

Philipp: Sure you can use the new Hugging Face Deep Learning Container. \nJeff:\
\ ok.\nJeff: and how can I get started? \nJeff: where can I find documentation?\
\ \nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face\n"
model-index:
  • name: bart-large-cnn-samsum

results:
- task:
type: summarization
name: Summarization
dataset:
name: 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization'
type: samsum
metrics:
- type: rogue-1
value: 42.621
name: Validation ROGUE-1
- type: rogue-2
value: 21.9825
name: Validation ROGUE-2
- type: rogue-l
value: 33.034
name: Validation ROGUE-L
- type: rogue-1
value: 41.3174
name: Test ROGUE-1
- type: rogue-2
value: 20.8716
name: Test ROGUE-2
- type: rogue-l
value: 32.1337
name: Test ROGUE-L
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 41.3282
name: ROUGE-1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTYzNzZkZDUzOWQzNGYxYTJhNGE4YWYyZjA0NzMyOWUzMDNhMmVhYzY1YTM0ZTJhYjliNGE4MDZhMjhhYjRkYSIsInZlcnNpb24iOjF9.OOM6l3v5rJCndmUIJV-2SDh2NjbPo5IgQOSL-Ju1Gwbi1voL5amsDEDOelaqlUBE3n55KkUsMLZhyn66yWxZBQ
- type: rouge
value: 20.8755
name: ROUGE-2
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWZiODFiYWQzY2NmOTc5YjA3NTI0YzQ1MzQ0ODk2NjgyMmVlMjA5MjZiNTJkMGRmZGEzN2M3MDNkMjkxMDVhYSIsInZlcnNpb24iOjF9.b8cPk2-IL24La3Vd0hhtii4tRXujh5urAwy6IVeTWHwYfXaURyC2CcQOWtlOx5bdO5KACeaJFrFBCGgjk-VGCQ
- type: rouge
value: 32.1353
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYWNmYzdiYWQ2ZWRkYzRiMGMxNWUwODgwZTdkY2NjZTc1NWE5NTFiMzU0OTU1N2JjN2ExYWQ2NGZkNjk5OTc4YSIsInZlcnNpb24iOjF9.Fzv4p-TEVicljiCqsBJHK1GsnE_AwGqamVmxTPI0WBNSIhZEhliRGmIL_z1pDq6WOzv3GN2YUGvhowU7GxnyAQ
- type: rouge
value: 38.401
name: ROUGE-LSUM
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNGI4MWY0NWMxMmQ0ODQ5MDhiNDczMDAzYzJkODBiMzgzYWNkMWM2YTZkZDJmNWJiOGQ3MmNjMGViN2UzYWI2ZSIsInZlcnNpb24iOjF9.7lw3h5k5lJ7tYFLZGUtLyDabFYd00l6ByhmvkW4fykocBy9Blyin4tdw4Xps4DW-pmrdMLgidHxBWz5MrSx1Bw
- type: loss
value: 1.4297215938568115
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzI0ZWNhNDM5YTViZDMyZGJjMDA1ZWFjYzNhOTdlOTFiNzhhMDBjNmM2MjA3ZmRkZjJjMjEyMGY3MzcwOTI2NyIsInZlcnNpb24iOjF9.oNaZsAtUDqGAqoZWJavlcW7PKx1AWsnkbhaQxadpOKk_u7ywJJabvTtzyx_DwEgZslgDETCf4MM-JKitZKjiDA
- type: gen_len
value: 60.0757
name: gen_len
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTgwYWYwMDRkNTJkMDM5N2I2MWNmYzQ3OWM1NDJmODUyZGViMGE4ZTdkNmIwYWM2N2VjZDNmN2RiMDE4YTYyYiIsInZlcnNpb24iOjF9.PbXTcNYX_SW-BuRQEcqyc21M7uKrOMbffQSAK6k2GLzTVRrzZxsDC57ktKL68zRY8fSiRGsnknOwv-nAR6YBCQ
---

bart-large-cnn-samsum

> If you want to use the model you should try a newer fine-tuned FLAN-T5 version philschmid/flan-t5-base-samsum out socring the BART version with +6 on ROGUE1 achieving 47.24.

TRY philschmid/flan-t5-base-samsum

This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.

For more information look at:





Hyperparameters

json
{
    "dataset_name": "samsum",
    "do_eval": true,
    "do_predict": true,
    "do_train": true,
    "fp16": true,
    "learning_rate": 5e-05,
    "model_name_or_path": "facebook/bart-large-cnn",
    "num_train_epochs": 3,
    "output_dir": "/opt/ml/model",
    "per_device_eval_batch_size": 4,
    "per_device_train_batch_size": 4,
    "predict_with_generate": true,
    "seed": 7
}

Usage

python
from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")

conversation = '''Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? 
Philipp: Sure you can use the new Hugging Face Deep Learning Container. 
Jeff: ok.
Jeff: and how can I get started? 
Jeff: where can I find documentation? 
Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face                                           
'''
summarizer(conversation)

Results

| key | value |
| --- | ----- |
| eval_rouge1 | 42.621 |
| eval_rouge2 | 21.9825 |
| eval_rougeL | 33.034 |
| eval_rougeLsum | 39.6783 |
| test_rouge1 | 41.3174 |
| test_rouge2 | 20.8716 |
| test_rougeL | 32.1337 |
| test_rougeLsum | 38.4149 |

View official source
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